code: def generate_data(self, sample_size):
      self.generation_process()
      confounder = np.random.randn(sample_size, len(self.confounder))
      effect_modifier = np.random.randn(
          sample_size, len(self.effect_modifier)
      )
      control_value = np.zeros((sample_size, len(self.treatment)))
      treatment_value = np.ones((sample_size, len(self.treatment)))
  
      treatment = self.nn["confounder=>treatment"].predict(confounder)
      treatment = treatment[:, np.newaxis]
      if self.treatment_is_binary:
          treatment = self.convert_to_binary(treatment)
  
      if treatment.ndim == 1:
          treatment = np.reshape(treatment, (-1, 1))
  
      outcome = (
          self.nn["confounder=>outcome"].predict(
              np.hstack((confounder, effect_modifier))
          )
          + self.nn["effect_modifier=>outcome"].predict(effect_modifier)
          + self.nn["treatment=>outcome"].predict(
              np.hstack((confounder, effect_modifier, treatment))
          )
      )
      y_control = (
          self.nn["confounder=>outcome"].predict(
              np.hstack((confounder, effect_modifier))
          )
          + self.nn["effect_modifier=>outcome"].predict(effect_modifier)
          + self.nn["treatment=>outcome"].predict(
              np.hstack((confounder, effect_modifier, control_value))
          )
      )
      y_treatment = (
          self.nn["confounder=>outcome"].predict(
              np.hstack((confounder, effect_modifier))
          )
          + self.nn["effect_modifier=>outcome"].predict(effect_modifier)
          + self.nn["treatment=>outcome"].predict(
              np.hstack((confounder, effect_modifier, treatment_value))
          )
      )
  
      # Understanding Neural Network weights
      # Refer to this link:https://stackoverflow.com/questions/50937628/mlp-classifier-neurons-weights
      self.weights = {
          key: self.nn[key].coefs_ for key in self.nn.keys()
      }
      self.bias = {
          key: self.nn[key].intercepts_ for key in self.nn.keys()
      }
  
      if outcome.ndim == 1:
          outcome = np.reshape(outcome, (-1, 1))
      if y_control.ndim == 1:
          y_control = np.reshape(y_control, (-1, 1))
      if y_treatment.ndim == 1:
          y_treatment = np.reshape(y_treatment, (-1, 1))
      self.true_value = np.mean(y_treatment - y_control, axis=0)
  
      return pd.DataFrame(
          np.hstack((effect_modifier, confounder, treatment, outcome)),
          columns=self.effect_modifier
          + self.confounder
          + self.treatment
          + self.outcome,
      )
  
file_path: dowhy/dowhy/utils/dgps/random_neural_network.py
imports: import numpy
  import pandas
  from sklearn.neural_network import MLPRegressor
  from dowhy.utils.dgp import DataGeneratingProcess
label_desc: Use image node_id 2 for calling the RandomNeuralNetwork obj's underlying member method code with example usage: obj.generate_data(sample_size) and returns: pd